A Statistical Learning Approach to Ultra-Reliable Low Latency Communication

By Marko Angjelichinoski, Kasper Fløe Trillingsgaard, Petar Popovski. 2018

74 citations recorded by the source corpus.

Mission-critical applications require Ultra-Reliable Low Latency (URLLC) wireless connections, where the packet error rate (PER) goes down to 10−9. Fulfillment of the bold reliability figures becomes meaningful only if it can be related to a statistical model in which the URLLC system operates. However, this model is generally not known and needs to be learned by sampling the wireless environment. In this paper, we treat this fundamental problem in the simplest possible communication-theoretic setting: selecting a transmission rate over a dynamic wireless channel in order to guarantee high transmission reliability. We introduce a novel statistical framework for design and assessment of URLLC systems, consisting of three key components: (i) channel model selection; (ii) learning the model using training; and (iii) selecting the transmission rate to satisfy the required reliability. As it is insufficient to specify the URLLC requirements only through PER, two types of statistical constraints are introduced, Averaged Reliability (AR) and Probably Correct Reliability (PCR). The analysis and the evaluations show that adequate model selection and learning are indispensable for designing consistent physical layer that asymptotically behaves as if the channel was known perfectly, while maintaining the reliability requirements in URLLC systems.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Wireless Access in Ultra-Reliable Low-Latency Communication (URLLC) (2018, methodology): “However, if the true channel differs even slightly from the assumed model (e.g. a small specular component is also present), the rate is not longer consistent and both the AR and PCR constraints will be violated, see [47] for in-depth discussions.”
A Very Brief Introduction to Machine Learning With Applications to Communication Systems (2018, background): “For example, designing a channel decoder based on samples obtained from a limited number of realizations of a given propagation channel requires the channel is stationary over a sufﬁciently long period of time (see [28]).”
Extreme URLLC: Vision, Challenges, and Key Enablers (2020, background): “5G URLLC is reactive in nature, and is built upon the availability of known, stationary channel and trafﬁc models , questioning the adequacy of the deﬁnition of reliability, as noted in [9].”